This page is aimed at technically experienced users and analytics teams. Here you manage automated data pipelines, precomputed analytical views, and connections to external BI tools (e.g. Metabase, Tableau, Power BI), and define how long certain data is retained.
What can I do here?
Create a data pipeline (ETL) that regularly moves data from a source into a destination table
Manually trigger a pipeline run and view its execution history
Create a precomputed view (materialized view) and refresh it manually if needed
Connect to an external BI tool, test the connection, and generate an API key
Define retention rules that automatically delete, archive, or anonymize old data
View the overall health (active/errored pipelines, storage used) on the overview page
Step by step
Create a pipeline: Switch to the "ETL" tab, click "New pipeline", choose a source type (database, event stream, API, file upload), a destination table and a schedule (cron expression), and save.
Run a pipeline manually: Click the play icon in the pipeline table to trigger an immediate run.
Create a view: Switch to the "Views & BI" tab, click "New view", and enter the underlying query along with a refresh schedule.
Set up a BI connection: In the same tab, click "New connection", choose the BI tool and the allowed tables, test the connection, and generate an API key if needed.
Create a retention rule: Switch to the "Retention" tab, click "New rule", and enter the target table, retention period in days, action (Delete/Archive/Anonymize), and an optional filter condition.
Fields explained
Field
Meaning
Notes/Impact
Source type (Pipeline)
Origin of the data
Database, Event stream, API, or File upload
Destination table
Table in the warehouse the data is written to
Should have a clear, unique name
Schedule (Cron)
Repeat interval for the pipeline or view refresh
In cron format, e.g. 0 * * * * for hourly
Source query (View)
SQL query that populates the view
Intended for technically experienced users only
Auto-refresh
Determines whether a view refreshes automatically on schedule
Can alternatively be triggered manually
BI tool type
Connection to Metabase, Tableau, Grafana, Power BI, or Looker
Determines the connection profile
Allowed tables
Tables the BI tool is permitted to access
Limits data access for security reasons
Target table (Retention)
Table the retention rule applies to
—
Retention days
How long records are kept before the action applies
—
Action (Retention)
Delete, Archive, or Anonymize
Determines what happens to expired data
Filter condition
Extra condition limiting which records are affected
Optional
Values & statuses
Pipeline status
Value
Plain meaning
Active
Pipeline runs according to its schedule
Paused
Pipeline is temporarily halted
Error
The last run failed
Draft
Pipeline is created but not yet activated
Run status (ETL run)
Value
Plain meaning
Running
The run is currently in progress
Completed
The run finished successfully
Failed
The run aborted with an error
Cancelled
The run was manually stopped
View status
Value
Plain meaning
Active
The view is up to date
Refreshing
The view is currently being recomputed
Stale
The view hasn't been refreshed for a while
Error
The last refresh failed
Retention action
Value
Plain meaning
Delete
Expired records are permanently removed
Archive
Expired records are moved to an archive
Anonymize
Personal data in expired records is removed, but the record remains
Frequently asked questions
Who is this page for? For technically experienced users and analytics/BI teams. For everyday use, the "Reports", "Dashboards" and "Data Explorer" pages are usually sufficient.
What happens if a pipeline fails? Its status changes to "Error" and you'll see an error message in the execution history. You can then manually rerun the pipeline.
How do I protect sensitive data when connecting a BI tool? Restrict the "Allowed tables" of that connection to exactly the tables the external tool actually needs.